Various embodiments of the invention relate generally to an inertial measurement system and particularly to measurement of the accuracy of orientation employed in the inertial measurement system.
Motion tracking systems, employed in an inertial measurement system or comprising the inertial measurement system, use motion sensors. Motion sensors are generally a gyroscope, an accelerometer, and a magnetometer sensor. The heading data, or the yaw orientation angle in the world coordinate system, is one of the key outputs from the motion tracking system in applications such as vehicle navigation or handset compass. The heading output is calculated from several motion sensors in the motion tracking system, the motion sensors being as follows: 3-axis gyroscope sensor, 3-axis accelerometer, and 3-axis magnetometer. Each sensor has 3-axis (x, y, z) measurement outputs, which are orthogonal to each other. The 3-axis measurement elements (yaw, roll and pitch) are aligned among the three sensors.
Among the three sensors, the accelerometer data is treated as the reference of the gravity direction (presumably the dominant component of the accelerometer data when the device movement is slow) and the magnetometer is treated as the reference to earth magnetic field direction (assuming no magnetic disturbance). Theoretically, the heading vector, i.e. the vector that points to the magnetic north, can be obtained by projecting the magnetometer data onto the plane that is perpendicular to the gravity vector. In reality, however, since the magnetometer suffers from environmental magnetic disturbance and the accelerometer senses other forms of accelerations together with gravity when the device is in motion, the magnetometer-accelerometer combination often leads to large heading error. Typically, the algorithms used in a sensor fusion, a part of the motion tracking system remedies the disturbance problem by leveraging the gyroscope. The gyroscope is immune to magnetic disturbance and linear acceleration interference when dynamically tracking the orientation change and while using the magnetometer and accelerometer data as the long-term reference to cancel the potential angle drift from the gyroscope imperfection.
Stated differently, motion tracking systems typically use a motion sensor that has distortion. The motion tracking system includes a motion tracking signal that has some uncertainty particularly because the motion tracking system is modeled as a stochastic system. For example, in calculating orientation, an error is calculated because once the error is known, it can be used as a measure of confidence in the calculated orientation. Currently, a rudimentary algorithm is used to calculate an estimate of the angle error. The algorithm is merely a scale of the confidence, such as discrete indicators, 1, 2, 3, and 4, with 1 indicating, for instance, low confidence to 4 indicating the most confidence within plus or minus 5 degrees. This is obviously not a precise estimate of the error. Further, in current systems, an estimate of the error cannot be put in a Kalman filter because an inverse function of a large matrix needs to be calculated thereby making the computation too complex to realize. Thus, there is effectively no error measurement provided in current systems, rather, a description of the error is provided. It is accordingly desirable to more accurately estimate the heading in a motion tracking system.